Transfer learning [1]} within the deep learning paradigm is a machine learning method where a neural network trained for a task is reused partially or entirely as the starting point to train or fine-tune a neural network on a second task.
Such methods were also proposed to limit the impact of lack of data when only small databases are available to train a neural network for a specific domain or task.
Transfer learning is widely used in Sentiment Analysis, where large databases are used to train generic cues which are fed into the training process, leading to better generalization abilities given limited training data [2]}.
